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Forward Deployed AI Engineer

EQ Bank | Equitable Bank
🇨🇦 Canada
Hybrid
Staff / Principal
2 months ago
  • AI
  • Azure
  • Claude
  • System Design
  • Design Thinking
  • Machine Learning
2 months ago
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We are looking for a Staff-level Forward Deployed AI Engineer to design, build, and deliver AI-powered applications that create measurable business impact.
This is a hands-on engineering role with strong design responsibility — you will spend most of your time writing code, integrating systems, and taking solutions to production, while also shaping practical, scalable designs that ensure what you build can operate reliably at enterprise scale.
You will work closely with business stakeholders to identify high-value opportunities, rapidly prototype solutions, and evolve them into well-architected, production-grade systems.

What You Will Be Responsible For:

    You will play a lead technical role in designing and delivering AI-enabled solutions across the enterprise.

    1. Build & Ship AI Applications (Primary Focus)

    • Design, develop, and deployAI-powered applications and workflows
    • Write production-quality code across:
      • Backend services and APIs
      • AI orchestration layers and agents
      • Enterprise integrations
      • Rapidly prototype solutions anditerate them into scalable production systems
      • Own deliveryend-to-end: build, test, deploy, monitor, and improve

        • 2. Design Practical, Scalable AI Systems

        • Translate use cases intoclear, implementable system designs
        • Make architecture decisions that balance:
          • Speed of delivery
          • Scalability and reliability
          • Cost and operational efficiency
          • Define patterns for:
            • API-first integrations
            • AI orchestration and workflows
            • Reusable services and components
            • Ensure systems aresimple enough to build quickly, butstructured enough to scale

              • 3. Integrate AI into Real Enterprise Workflows

              • Embed LLM capabilities intoproducts, internal tools, and business processes
              • Build and maintainAPIs and system integrations
              • Implementagent workflows and orchestration logic that solve real operational problems
              • Optimize systems forperformance, resilience, and cost efficiency

        • 4. Partner with Business & Deliver Outcomes

        • Work directly with stakeholders tounderstand problems and validate solutions
        • Translate requirements intoworking software quickly (days/weeks, not months)
        • Iterate based on feedback and usage to drivemeasurable impact

          • 5. Contribute to Engineering Standards & Reuse

          • Build and contribute toshared libraries, templates, and services
          • Establishpractical patterns based on real implementations
          • Help evolve internal platforms throughcode and working solutions, not just design artifacts

            • 6. Build Within a Governed AI Environment

            • Implementsecure and reliable AI solutions in practice, including:
              • Prompt safety and validation
              • Injection/misuse prevention
              • Observability and traceability
              • Align implementations withenterprise security, privacy, and compliance requirements

                • Technology Environment

                • Cloud & Platform: Microsoft ecosystem (Azure)
                • AI Models: Claude and other enterprise-approved LLMs
                • Architecture Style: API-first, event-driven, and modular services
                • Core Focus:
                  • AI application engineering
                  • Orchestration and agent workflows
                  • Enterprise integrations

What you bring:

    Hands-On Engineering Strength (Critical)

    • Proven ability tobuild and ship production systems at scale
    • Strong experience in:
      • Backend development and API design
      • Cloud-native systems (Azure preferred)
      • Integration-heavy, distributed applications
      • Comfortable operating in ahigh-output, hands-on environment

        • System Design & Architecture Judgment

        • Ability to designclean, practical architectures that support real-world constraints
        • Experience making trade-offs across:
          • delivery speed vs scalability
          • simplicity vs flexibility
          • Can move fluidly betweencoding and design thinking

            • AI / GenAI Development

            • Hands-on experience buildingLLM-powered applications in production
            • Strong understanding of:
              • Prompt design and evaluation
              • Agent-based workflows and orchestration
              • Integrating AI into production systems
              • Ability todebug, tune, and improve AI behavior in code

                • Execution Mindset

                • Bias towardshipping and learning from production usage
                • Comfortable moving fromidea → prototype → production
                • Strong ownership:you build it, you run it

Forward Deployed AI Engineer · EQ Bank | Equitable Bank

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